chore: bump to 0.12.1 (#11122)

Signed-off-by: yihong0618 <zouzou0208@gmail.com>

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.11.2",
+        default="0.12.1",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/console/app/app.py

@@ -190,7 +190,7 @@ class AppCopyApi(Resource):
             )
             session.commit()
 
-            stmt = select(App).where(App.id == result.app.id)
+            stmt = select(App).where(App.id == result.app_id)
             app = session.scalar(stmt)
 
         return app, 201

api/core/app/apps/advanced_chat/app_generator.py

@@ -127,7 +127,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             conversation_id=conversation.id if conversation else None,
             inputs=conversation.inputs
             if conversation
-            else self._prepare_user_inputs(user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.id),
+            else self._prepare_user_inputs(
+                user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
+            ),
             query=query,
             files=file_objs,
             parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,

api/core/app/apps/agent_chat/app_generator.py

@@ -134,7 +134,9 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             conversation_id=conversation.id if conversation else None,
             inputs=conversation.inputs
             if conversation
-            else self._prepare_user_inputs(user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.id),
+            else self._prepare_user_inputs(
+                user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
+            ),
             query=query,
             files=file_objs,
             parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,

api/core/app/apps/chat/app_generator.py

@@ -132,7 +132,9 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             conversation_id=conversation.id if conversation else None,
             inputs=conversation.inputs
             if conversation
-            else self._prepare_user_inputs(user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.id),
+            else self._prepare_user_inputs(
+                user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
+            ),
             query=query,
             files=file_objs,
             parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,

api/core/app/apps/completion/app_generator.py

@@ -114,7 +114,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             model_conf=ModelConfigConverter.convert(app_config),
             file_upload_config=file_extra_config,
             inputs=self._prepare_user_inputs(
-                user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.id
+                user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
             ),
             query=query,
             files=file_objs,

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -3,6 +3,7 @@ import time
 from collections.abc import Mapping, Sequence
 from datetime import UTC, datetime
 from typing import Any, Optional, Union, cast
+from uuid import uuid4
 
 from sqlalchemy.orm import Session
 
@@ -80,38 +81,38 @@ class WorkflowCycleManage:
 
             inputs[f"sys.{key.value}"] = value
 
-        inputs = WorkflowEntry.handle_special_values(inputs)
-
         triggered_from = (
             WorkflowRunTriggeredFrom.DEBUGGING
             if self._application_generate_entity.invoke_from == InvokeFrom.DEBUGGER
             else WorkflowRunTriggeredFrom.APP_RUN
         )
 
-        # init workflow run
-        workflow_run = WorkflowRun()
-        workflow_run_id = self._workflow_system_variables[SystemVariableKey.WORKFLOW_RUN_ID]
-        if workflow_run_id:
-            workflow_run.id = workflow_run_id
-        workflow_run.tenant_id = self._workflow.tenant_id
-        workflow_run.app_id = self._workflow.app_id
-        workflow_run.sequence_number = new_sequence_number
-        workflow_run.workflow_id = self._workflow.id
-        workflow_run.type = self._workflow.type
-        workflow_run.triggered_from = triggered_from.value
-        workflow_run.version = self._workflow.version
-        workflow_run.graph = self._workflow.graph
-        workflow_run.inputs = json.dumps(inputs)
-        workflow_run.status = WorkflowRunStatus.RUNNING.value
-        workflow_run.created_by_role = (
-            CreatedByRole.ACCOUNT.value if isinstance(self._user, Account) else CreatedByRole.END_USER.value
-        )
-        workflow_run.created_by = self._user.id
+        # handle special values
+        inputs = WorkflowEntry.handle_special_values(inputs)
 
-        db.session.add(workflow_run)
-        db.session.commit()
-        db.session.refresh(workflow_run)
-        db.session.close()
+        # init workflow run
+        with Session(db.engine, expire_on_commit=False) as session:
+            workflow_run = WorkflowRun()
+            system_id = self._workflow_system_variables[SystemVariableKey.WORKFLOW_RUN_ID]
+            workflow_run.id = system_id or str(uuid4())
+            workflow_run.tenant_id = self._workflow.tenant_id
+            workflow_run.app_id = self._workflow.app_id
+            workflow_run.sequence_number = new_sequence_number
+            workflow_run.workflow_id = self._workflow.id
+            workflow_run.type = self._workflow.type
+            workflow_run.triggered_from = triggered_from.value
+            workflow_run.version = self._workflow.version
+            workflow_run.graph = self._workflow.graph
+            workflow_run.inputs = json.dumps(inputs)
+            workflow_run.status = WorkflowRunStatus.RUNNING
+            workflow_run.created_by_role = (
+                CreatedByRole.ACCOUNT if isinstance(self._user, Account) else CreatedByRole.END_USER
+            )
+            workflow_run.created_by = self._user.id
+            workflow_run.created_at = datetime.now(UTC).replace(tzinfo=None)
+
+            session.add(workflow_run)
+            session.commit()
 
         return workflow_run
 

api/core/indexing_runner.py

@@ -720,8 +720,10 @@ class IndexingRunner:
 
             tokens = 0
             if embedding_model_instance:
-                page_content_list = [document.page_content for document in chunk_documents]
-                tokens += sum(embedding_model_instance.get_text_embedding_num_tokens(page_content_list))
+                tokens += sum(
+                    embedding_model_instance.get_text_embedding_num_tokens([document.page_content])
+                    for document in chunk_documents
+                )
 
             # load index
             index_processor.load(dataset, chunk_documents, with_keywords=False)

api/core/model_manager.py

@@ -183,7 +183,7 @@ class ModelInstance:
             input_type=input_type,
         )
 
-    def get_text_embedding_num_tokens(self, texts: list[str]) -> list[int]:
+    def get_text_embedding_num_tokens(self, texts: list[str]) -> int:
         """
         Get number of tokens for text embedding
 

api/core/model_runtime/model_providers/anthropic/llm/llm.py

@@ -453,7 +453,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
         return credentials_kwargs
 
-    def _convert_prompt_messages(self, prompt_messages: list[PromptMessage]) -> tuple[str, list[dict]]:
+    def _convert_prompt_messages(self, prompt_messages: Sequence[PromptMessage]) -> tuple[str, list[dict]]:
         """
         Convert prompt messages to dict list and system
         """
@@ -461,7 +461,15 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         first_loop = True
         for message in prompt_messages:
             if isinstance(message, SystemPromptMessage):
-                message.content = message.content.strip()
+                if isinstance(message.content, str):
+                    message.content = message.content.strip()
+                elif isinstance(message.content, list):
+                    # System prompt only support text
+                    message.content = "".join(
+                        c.data.strip() for c in message.content if isinstance(c, TextPromptMessageContent)
+                    )
+                else:
+                    raise ValueError(f"Unknown system prompt message content type {type(message.content)}")
                 if first_loop:
                     system = message.content
                     first_loop = False

api/core/model_runtime/model_providers/azure_openai/_constant.py

@@ -779,7 +779,7 @@ LLM_BASE_MODELS = [
                     name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
-                _get_max_tokens(default=512, min_val=1, max_val=4096),
+                _get_max_tokens(default=512, min_val=1, max_val=16384),
                 ParameterRule(
                     name="seed",
                     label=I18nObject(zh_Hans="种子", en_US="Seed"),

api/core/model_runtime/model_providers/azure_openai/tts/tts.py

@@ -14,7 +14,7 @@ from core.model_runtime.model_providers.azure_openai._constant import TTS_BASE_M
 
 class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
     """
-    Model class for OpenAI Speech to text model.
+    Model class for OpenAI text2speech model.
     """
 
     def _invoke(

api/core/model_runtime/model_providers/gitee_ai/rerank/rerank.py

@@ -122,7 +122,7 @@ class GiteeAIRerankModel(RerankModel):
             label=I18nObject(en_US=model),
             model_type=ModelType.RERANK,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512))},
         )
 
         return entity

api/core/model_runtime/model_providers/gitee_ai/tts/tts.py

@@ -10,7 +10,7 @@ from core.model_runtime.model_providers.gitee_ai._common import _CommonGiteeAI
 
 class GiteeAIText2SpeechModel(_CommonGiteeAI, TTSModel):
     """
-    Model class for OpenAI Speech to text model.
+    Model class for OpenAI text2speech model.
     """
 
     def _invoke(

api/core/model_runtime/model_providers/gpustack/rerank/rerank.py

@@ -140,7 +140,7 @@ class GPUStackRerankModel(RerankModel):
             label=I18nObject(en_US=model),
             model_type=ModelType.RERANK,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512))},
         )
 
         return entity

api/core/model_runtime/model_providers/jina/rerank/rerank.py

@@ -128,7 +128,7 @@ class JinaRerankModel(RerankModel):
             label=I18nObject(en_US=model),
             model_type=ModelType.RERANK,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000))},
         )
 
         return entity

api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py

@@ -193,7 +193,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
             label=I18nObject(en_US=model),
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000))},
         )
 
         return entity

api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py

@@ -139,7 +139,7 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

api/core/model_runtime/model_providers/openai/llm/llm.py

@@ -943,6 +943,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 }
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
+            if isinstance(message.content, list):
+                text_contents = filter(lambda c: isinstance(c, TextPromptMessageContent), message.content)
+                message.content = "".join(c.data for c in text_contents)
             message_dict = {"role": "system", "content": message.content}
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)

api/core/model_runtime/model_providers/openai/tts/tts.py

@@ -11,7 +11,7 @@ from core.model_runtime.model_providers.openai._common import _CommonOpenAI
 
 class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
     """
-    Model class for OpenAI Speech to text model.
+    Model class for OpenAI text2speech model.
     """
 
     def _invoke(

api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.yaml

@@ -9,6 +9,7 @@ supported_model_types:
   - text-embedding
   - speech2text
   - rerank
+  - tts
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -67,7 +68,7 @@ model_credential_schema:
         - variable: __model_type
           value: llm
       type: text-input
-      default: '4096'
+      default: "4096"
       placeholder:
         zh_Hans: 在此输入您的模型上下文长度
         en_US: Enter your Model context size
@@ -80,7 +81,7 @@ model_credential_schema:
         - variable: __model_type
           value: text-embedding
       type: text-input
-      default: '4096'
+      default: "4096"
       placeholder:
         zh_Hans: 在此输入您的模型上下文长度
         en_US: Enter your Model context size
@@ -93,7 +94,7 @@ model_credential_schema:
         - variable: __model_type
           value: rerank
       type: text-input
-      default: '4096'
+      default: "4096"
       placeholder:
         zh_Hans: 在此输入您的模型上下文长度
         en_US: Enter your Model context size
@@ -104,7 +105,7 @@ model_credential_schema:
       show_on:
         - variable: __model_type
           value: llm
-      default: '4096'
+      default: "4096"
       type: text-input
     - variable: function_calling_type
       show_on:
@@ -174,3 +175,19 @@ model_credential_schema:
           value: llm
       default: '\n\n'
       type: text-input
+    - variable: voices
+      show_on:
+        - variable: __model_type
+          value: tts
+      label:
+        en_US: Available Voices (comma-separated)
+        zh_Hans: 可用声音（用英文逗号分隔）
+      type: text-input
+      required: false
+      default: "alloy"
+      placeholder:
+        en_US: "alloy,echo,fable,onyx,nova,shimmer"
+        zh_Hans: "alloy,echo,fable,onyx,nova,shimmer"
+      help:
+        en_US: "List voice names separated by commas. First voice will be used as default."
+        zh_Hans: "用英文逗号分隔的声音列表。第一个声音将作为默认值。"

api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py

@@ -176,7 +176,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

api/core/model_runtime/model_providers/openai_api_compatible/tts/tts.py

@@ -0,0 +1,145 @@
+from collections.abc import Iterable
+from typing import Optional
+from urllib.parse import urljoin
+
+import requests
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType
+from core.model_runtime.errors.invoke import InvokeBadRequestError
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.tts_model import TTSModel
+from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOaiApiCompat
+
+
+class OAICompatText2SpeechModel(_CommonOaiApiCompat, TTSModel):
+    """
+    Model class for OpenAI-compatible text2speech model.
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        tenant_id: str,
+        credentials: dict,
+        content_text: str,
+        voice: str,
+        user: Optional[str] = None,
+    ) -> Iterable[bytes]:
+        """
+        Invoke TTS model
+
+        :param model: model name
+        :param tenant_id: user tenant id
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: model voice/speaker
+        :param user: unique user id
+        :return: audio data as bytes iterator
+        """
+        # Set up headers with authentication if provided
+        headers = {}
+        if api_key := credentials.get("api_key"):
+            headers["Authorization"] = f"Bearer {api_key}"
+
+        # Construct endpoint URL
+        endpoint_url = credentials.get("endpoint_url")
+        if not endpoint_url.endswith("/"):
+            endpoint_url += "/"
+        endpoint_url = urljoin(endpoint_url, "audio/speech")
+
+        # Get audio format from model properties
+        audio_format = self._get_model_audio_type(model, credentials)
+
+        # Split text into chunks if needed based on word limit
+        word_limit = self._get_model_word_limit(model, credentials)
+        sentences = self._split_text_into_sentences(content_text, word_limit)
+
+        for sentence in sentences:
+            # Prepare request payload
+            payload = {"model": model, "input": sentence, "voice": voice, "response_format": audio_format}
+
+            # Make POST request
+            response = requests.post(endpoint_url, headers=headers, json=payload, stream=True)
+
+            if response.status_code != 200:
+                raise InvokeBadRequestError(response.text)
+
+            # Stream the audio data
+            for chunk in response.iter_content(chunk_size=4096):
+                if chunk:
+                    yield chunk
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            # Get default voice for validation
+            voice = self._get_model_default_voice(model, credentials)
+
+            # Test with a simple text
+            next(
+                self._invoke(
+                    model=model, tenant_id="validate", credentials=credentials, content_text="Test.", voice=voice
+                )
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
+        """
+        Get customizable model schema
+        """
+        # Parse voices from comma-separated string
+        voice_names = credentials.get("voices", "alloy").strip().split(",")
+        voices = []
+
+        for voice in voice_names:
+            voice = voice.strip()
+            if not voice:
+                continue
+
+            # Use en-US for all voices
+            voices.append(
+                {
+                    "name": voice,
+                    "mode": voice,
+                    "language": "en-US",
+                }
+            )
+
+        # If no voices provided or all voices were empty strings, use 'alloy' as default
+        if not voices:
+            voices = [{"name": "Alloy", "mode": "alloy", "language": "en-US"}]
+
+        return AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TTS,
+            model_properties={
+                ModelPropertyKey.AUDIO_TYPE: credentials.get("audio_type", "mp3"),
+                ModelPropertyKey.WORD_LIMIT: int(credentials.get("word_limit", 4096)),
+                ModelPropertyKey.DEFAULT_VOICE: voices[0]["mode"],
+                ModelPropertyKey.VOICES: voices,
+            },
+        )
+
+    def get_tts_model_voices(self, model: str, credentials: dict, language: Optional[str] = None) -> list:
+        """
+        Override base get_tts_model_voices to handle customizable voices
+        """
+        model_schema = self.get_customizable_model_schema(model, credentials)
+
+        if not model_schema or ModelPropertyKey.VOICES not in model_schema.model_properties:
+            raise ValueError("this model does not support voice")
+
+        voices = model_schema.model_properties[ModelPropertyKey.VOICES]
+
+        # Always return all voices regardless of language
+        return [{"name": d["name"], "value": d["mode"]} for d in voices]

api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py

@@ -182,7 +182,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py

@@ -173,7 +173,7 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

api/core/model_runtime/model_providers/voyage/text_embedding/text_embedding.py

@@ -166,7 +166,7 @@ class VoyageTextEmbeddingModel(TextEmbeddingModel):
             label=I18nObject(en_US=model),
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512))},
         )
 
         return entity

api/core/model_runtime/model_providers/x/llm/grok-beta.yaml

@@ -1,9 +1,12 @@
 model: grok-beta
 label:
-  en_US: Grok beta
+  en_US: Grok Beta
 model_type: llm
 features:
+  - agent-thought
+  - tool-call
   - multi-tool-call
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 131072

api/core/model_runtime/model_providers/x/llm/grok-vision-beta.yaml

@@ -0,0 +1,64 @@
+model: grok-vision-beta
+label:
+  en_US: Grok Vision Beta
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 2.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: 0
+    max: 2.0
+    precision: 1
+    required: false
+    help:
+      en_US: "Number between 0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
+      zh_Hans: "介于0和2.0之间的数字。正值会根据新标记在文本中迄今为止的现有频率来惩罚它们，从而降低模型一字不差地重复同一句话的可能性。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

api/core/model_runtime/model_providers/x/llm/llm.py

@@ -35,3 +35,5 @@ class XAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
         credentials["endpoint_url"] = str(URL(credentials["endpoint_url"])) or "https://api.x.ai/v1"
         credentials["mode"] = LLMMode.CHAT.value
         credentials["function_calling_type"] = "tool_call"
+        credentials["stream_function_calling"] = "support"
+        credentials["vision_support"] = "support"

api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py

@@ -105,17 +105,6 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
 
         return [list(map(float, e)) for e in embeddings], embedding_used_tokens
 
-    def embed_query(self, text: str) -> list[float]:
-        """Call out to ZhipuAI's embedding endpoint.
-
-        Args:
-            text: The text to embed.
-
-        Returns:
-            Embeddings for the text.
-        """
-        return self.embed_documents([text])[0]
-
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage

api/core/ops/ops_trace_manager.py

@@ -445,7 +445,7 @@ class TraceTask:
             "ls_provider": message_data.model_provider,
             "ls_model_name": message_data.model_id,
             "status": message_data.status,
-            "from_end_user_id": message_data.from_account_id,
+            "from_end_user_id": message_data.from_end_user_id,
             "from_account_id": message_data.from_account_id,
             "agent_based": message_data.agent_based,
             "workflow_run_id": message_data.workflow_run_id,
@@ -521,7 +521,7 @@ class TraceTask:
             "ls_provider": message_data.model_provider,
             "ls_model_name": message_data.model_id,
             "status": message_data.status,
-            "from_end_user_id": message_data.from_account_id,
+            "from_end_user_id": message_data.from_end_user_id,
             "from_account_id": message_data.from_account_id,
             "agent_based": message_data.agent_based,
             "workflow_run_id": message_data.workflow_run_id,
@@ -570,7 +570,7 @@ class TraceTask:
             "ls_provider": message_data.model_provider,
             "ls_model_name": message_data.model_id,
             "status": message_data.status,
-            "from_end_user_id": message_data.from_account_id,
+            "from_end_user_id": message_data.from_end_user_id,
             "from_account_id": message_data.from_account_id,
             "agent_based": message_data.agent_based,
             "workflow_run_id": message_data.workflow_run_id,

api/core/rag/docstore/dataset_docstore.py

@@ -78,13 +78,8 @@ class DatasetDocumentStore:
                 model_type=ModelType.TEXT_EMBEDDING,
                 model=self._dataset.embedding_model,
             )
-        if embedding_model:
-            page_content_list = [doc.page_content for doc in docs]
-            tokens_list = embedding_model.get_text_embedding_num_tokens(page_content_list)
-        else:
-            tokens_list = [0] * len(docs)
 
-        for doc, tokens in zip(docs, tokens_list):
+        for doc in docs:
             if not isinstance(doc, Document):
                 raise ValueError("doc must be a Document")
 
@@ -96,6 +91,12 @@ class DatasetDocumentStore:
                     f"doc_id {doc.metadata['doc_id']} already exists. Set allow_update to True to overwrite."
                 )
 
+            # calc embedding use tokens
+            if embedding_model:
+                tokens = embedding_model.get_text_embedding_num_tokens(texts=[doc.page_content])
+            else:
+                tokens = 0
+
             if not segment_document:
                 max_position += 1
 

api/core/rag/splitter/fixed_text_splitter.py

@@ -65,9 +65,8 @@ class FixedRecursiveCharacterTextSplitter(EnhanceRecursiveCharacterTextSplitter)
             chunks = [text]
 
         final_chunks = []
-        chunks_lengths = self._length_function(chunks)
-        for chunk, chunk_length in zip(chunks, chunks_lengths):
-            if chunk_length > self._chunk_size:
+        for chunk in chunks:
+            if self._length_function(chunk) > self._chunk_size:
                 final_chunks.extend(self.recursive_split_text(chunk))
             else:
                 final_chunks.append(chunk)
@@ -94,8 +93,8 @@ class FixedRecursiveCharacterTextSplitter(EnhanceRecursiveCharacterTextSplitter)
         # Now go merging things, recursively splitting longer texts.
         _good_splits = []
         _good_splits_lengths = []  # cache the lengths of the splits
-        s_lens = self._length_function(splits)
-        for s, s_len in zip(splits, s_lens):
+        for s in splits:
+            s_len = self._length_function(s)
             if s_len < self._chunk_size:
                 _good_splits.append(s)
                 _good_splits_lengths.append(s_len)

api/core/rag/splitter/text_splitter.py

@@ -45,7 +45,7 @@ class TextSplitter(BaseDocumentTransformer, ABC):
         self,
         chunk_size: int = 4000,
         chunk_overlap: int = 200,
-        length_function: Callable[[str], [int]] = len,
+        length_function: Callable[[str], int] = len,
         keep_separator: bool = False,
         add_start_index: bool = False,
     ) -> None:
@@ -224,8 +224,8 @@ class CharacterTextSplitter(TextSplitter):
         splits = _split_text_with_regex(text, self._separator, self._keep_separator)
         _separator = "" if self._keep_separator else self._separator
         _good_splits_lengths = []  # cache the lengths of the splits
-        if splits:
-            _good_splits_lengths.extend(self._length_function(splits))
+        for split in splits:
+            _good_splits_lengths.append(self._length_function(split))
         return self._merge_splits(splits, _separator, _good_splits_lengths)
 
 
@@ -478,8 +478,9 @@ class RecursiveCharacterTextSplitter(TextSplitter):
         _good_splits = []
         _good_splits_lengths = []  # cache the lengths of the splits
         _separator = "" if self._keep_separator else separator
-        s_lens = self._length_function(splits)
-        for s, s_len in zip(splits, s_lens):
+
+        for s in splits:
+            s_len = self._length_function(s)
             if s_len < self._chunk_size:
                 _good_splits.append(s)
                 _good_splits_lengths.append(s_len)

api/core/tools/provider/builtin/chart/chart.py

@@ -1,3 +1,4 @@
+import matplotlib
 import matplotlib.pyplot as plt
 from matplotlib.font_manager import FontProperties, fontManager
 
@@ -5,7 +6,7 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 
 
 def set_chinese_font():
-    font_list = [
+    to_find_fonts = [
         "PingFang SC",
         "SimHei",
         "Microsoft YaHei",
@@ -15,16 +16,16 @@ def set_chinese_font():
         "Noto Sans CJK SC",
         "Noto Sans CJK JP",
     ]
-
-    for font in font_list:
-        if font in fontManager.ttflist:
-            chinese_font = FontProperties(font)
-            if chinese_font.get_name() == font:
-                return chinese_font
+    installed_fonts = frozenset(fontInfo.name for fontInfo in fontManager.ttflist)
+    for font in to_find_fonts:
+        if font in installed_fonts:
+            return FontProperties(font)
 
     return FontProperties()
 
 
+# use non-interactive backend to prevent `RuntimeError: main thread is not in main loop`
+matplotlib.use("Agg")
 # use a business theme
 plt.style.use("seaborn-v0_8-darkgrid")
 plt.rcParams["axes.unicode_minus"] = False

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_img.py

@@ -18,6 +18,12 @@ class DuckDuckGoImageSearchTool(BuiltinTool):
             "size": tool_parameters.get("size"),
             "max_results": tool_parameters.get("max_results"),
         }
+
+        # Add query_prefix handling
+        query_prefix = tool_parameters.get("query_prefix", "").strip()
+        final_query = f"{query_prefix} {query_dict['keywords']}".strip()
+        query_dict["keywords"] = final_query
+
         response = DDGS().images(**query_dict)
         markdown_result = "\n\n"
         json_result = []

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_img.yaml

@@ -86,3 +86,14 @@ parameters:
       en_US: The size of the image to be searched.
       zh_Hans: 要搜索的图片的大小
     form: form
+  - name: query_prefix
+    label:
+      en_US: Query Prefix
+      zh_Hans: 查询前缀
+    type: string
+    required: false
+    default: ""
+    form: form
+    human_description:
+      en_US: Specific Search e.g. "site:unsplash.com"
+      zh_Hans: 定向搜索 e.g. "site:unsplash.com"

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_news.py

@@ -7,7 +7,7 @@ from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.tool.builtin_tool import BuiltinTool
 
 SUMMARY_PROMPT = """
-User's query: 
+User's query:
 {query}
 
 Here are the news results:
@@ -30,6 +30,12 @@ class DuckDuckGoNewsSearchTool(BuiltinTool):
             "safesearch": "moderate",
             "region": "wt-wt",
         }
+
+        # Add query_prefix handling
+        query_prefix = tool_parameters.get("query_prefix", "").strip()
+        final_query = f"{query_prefix} {query_dict['keywords']}".strip()
+        query_dict["keywords"] = final_query
+
         try:
             response = list(DDGS().news(**query_dict))
             if not response:

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_news.yaml

@@ -69,3 +69,14 @@ parameters:
       en_US: Whether to pass the news results to llm for summarization.
       zh_Hans: 是否需要将新闻结果传给大模型总结
     form: form
+  - name: query_prefix
+    label:
+      en_US: Query Prefix
+      zh_Hans: 查询前缀
+    type: string
+    required: false
+    default: ""
+    form: form
+    human_description:
+      en_US: Specific Search e.g. "site:msn.com"
+      zh_Hans: 定向搜索 e.g. "site:msn.com"

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_search.py

@@ -7,7 +7,7 @@ from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.tool.builtin_tool import BuiltinTool
 
 SUMMARY_PROMPT = """
-User's query: 
+User's query:
 {query}
 
 Here is the search engine result:
@@ -26,7 +26,12 @@ class DuckDuckGoSearchTool(BuiltinTool):
         query = tool_parameters.get("query")
         max_results = tool_parameters.get("max_results", 5)
         require_summary = tool_parameters.get("require_summary", False)
-        response = DDGS().text(query, max_results=max_results)
+
+        # Add query_prefix handling
+        query_prefix = tool_parameters.get("query_prefix", "").strip()
+        final_query = f"{query_prefix} {query}".strip()
+
+        response = DDGS().text(final_query, max_results=max_results)
         if require_summary:
             results = "\n".join([res.get("body") for res in response])
             results = self.summary_results(user_id=user_id, content=results, query=query)

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_search.yaml

@@ -39,3 +39,14 @@ parameters:
       en_US: Whether to pass the search results to llm for summarization.
       zh_Hans: 是否需要将搜索结果传给大模型总结
     form: form
+  - name: query_prefix
+    label:
+      en_US: Query Prefix
+      zh_Hans: 查询前缀
+    type: string
+    required: false
+    default: ""
+    form: form
+    human_description:
+      en_US: Specific Search e.g. "site:wikipedia.org"
+      zh_Hans: 定向搜索 e.g. "site:wikipedia.org"

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_video.py

@@ -24,7 +24,7 @@ max-width: 100%; border-radius: 8px;">
 
     def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> list[ToolInvokeMessage]:
         query_dict = {
-            "keywords": tool_parameters.get("query"),
+            "keywords": tool_parameters.get("query"),  # LLM's query
             "region": tool_parameters.get("region", "wt-wt"),
             "safesearch": tool_parameters.get("safesearch", "moderate"),
             "timelimit": tool_parameters.get("timelimit"),
@@ -40,6 +40,12 @@ max-width: 100%; border-radius: 8px;">
         # Get proxy URL from parameters
         proxy_url = tool_parameters.get("proxy_url", "").strip()
 
+        query_prefix = tool_parameters.get("query_prefix", "").strip()
+        final_query = f"{query_prefix} {query_dict['keywords']}".strip()
+
+        # Update the keywords in query_dict with the final_query
+        query_dict["keywords"] = final_query
+
         response = DDGS().videos(**query_dict)
 
         # Create HTML result with embedded iframes
@@ -51,9 +57,13 @@ max-width: 100%; border-radius: 8px;">
             embed_html = res.get("embed_html", "")
             description = res.get("description", "")
             content_url = res.get("content", "")
+            transcript_url = None
 
             # Handle TED.com videos
-            if not embed_html and "ted.com/talks" in content_url:
+            if "ted.com/talks" in content_url:
+                # Create transcript URL
+                transcript_url = f"{content_url}/transcript"
+                # Create embed URL
                 embed_url = content_url.replace("www.ted.com", "embed.ted.com")
                 if proxy_url:
                     embed_url = f"{proxy_url}{embed_url}"
@@ -68,8 +78,14 @@ max-width: 100%; border-radius: 8px;">
 
             markdown_result += f"{title}\n\n"
             markdown_result += f"{embed_html}\n\n"
+            if description:
+                markdown_result += f"{description}\n\n"
             markdown_result += "---\n\n"
 
-            json_result.append(self.create_json_message(res))
+            # Add transcript_url to the JSON result if available
+            result_dict = res.copy()
+            if transcript_url:
+                result_dict["transcript_url"] = transcript_url
+            json_result.append(self.create_json_message(result_dict))
 
         return [self.create_text_message(markdown_result)] + json_result

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_video.yaml

@@ -95,3 +95,14 @@ parameters:
       en_US: Proxy URL
       zh_Hans: 视频代理地址
     form: form
+  - name: query_prefix
+    label:
+      en_US: Query Prefix
+      zh_Hans: 查询前缀
+    type: string
+    required: false
+    default: ""
+    form: form
+    human_description:
+      en_US: Specific Search e.g. "site:www.ted.com"
+      zh_Hans: 定向搜索 e.g. "site:www.ted.com"

api/core/tools/provider/builtin/searchapi/tools/google.py

@@ -45,7 +45,7 @@ class SearchAPI:
     def _process_response(res: dict, type: str) -> str:
         """Process response from SearchAPI."""
         if "error" in res:
-            raise ValueError(f"Got error from SearchApi: {res['error']}")
+            return res["error"]
 
         toret = ""
         if type == "text":

api/core/tools/provider/builtin/searchapi/tools/google_jobs.py

@@ -45,7 +45,7 @@ class SearchAPI:
     def _process_response(res: dict, type: str) -> str:
         """Process response from SearchAPI."""
         if "error" in res:
-            raise ValueError(f"Got error from SearchApi: {res['error']}")
+            return res["error"]
 
         toret = ""
         if type == "text":

api/core/tools/provider/builtin/searchapi/tools/google_news.py

@@ -45,7 +45,7 @@ class SearchAPI:
     def _process_response(res: dict, type: str) -> str:
         """Process response from SearchAPI."""
         if "error" in res:
-            raise ValueError(f"Got error from SearchApi: {res['error']}")
+            return res["error"]
 
         toret = ""
         if type == "text":

api/core/tools/provider/builtin/searchapi/tools/youtube_transcripts.py

@@ -45,7 +45,7 @@ class SearchAPI:
     def _process_response(res: dict) -> str:
         """Process response from SearchAPI."""
         if "error" in res:
-            raise ValueError(f"Got error from SearchApi: {res['error']}")
+            return res["error"]
 
         toret = ""
         if "transcripts" in res and "text" in res["transcripts"][0]:

api/core/workflow/nodes/llm/node.py

@@ -20,6 +20,7 @@ from core.model_runtime.entities import (
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
+    PromptMessageContent,
     PromptMessageRole,
     SystemPromptMessage,
     UserPromptMessage,
@@ -66,7 +67,6 @@ from .entities import (
     ModelConfig,
 )
 from .exc import (
-    FileTypeNotSupportError,
     InvalidContextStructureError,
     InvalidVariableTypeError,
     LLMModeRequiredError,
@@ -137,12 +137,12 @@ class LLMNode(BaseNode[LLMNodeData]):
             query = None
             if self.node_data.memory:
                 query = self.node_data.memory.query_prompt_template
-            if query is None and (
-                query_variable := self.graph_runtime_state.variable_pool.get(
-                    (SYSTEM_VARIABLE_NODE_ID, SystemVariableKey.QUERY)
-                )
-            ):
-                query = query_variable.text
+                if not query and (
+                    query_variable := self.graph_runtime_state.variable_pool.get(
+                        (SYSTEM_VARIABLE_NODE_ID, SystemVariableKey.QUERY)
+                    )
+                ):
+                    query = query_variable.text
 
             prompt_messages, stop = self._fetch_prompt_messages(
                 user_query=query,
@@ -675,7 +675,7 @@ class LLMNode(BaseNode[LLMNodeData]):
                             and ModelFeature.AUDIO not in model_config.model_schema.features
                         )
                     ):
-                        raise FileTypeNotSupportError(type_name=content_item.type)
+                        continue
                     prompt_message_content.append(content_item)
                 if len(prompt_message_content) == 1 and prompt_message_content[0].type == PromptMessageContentType.TEXT:
                     prompt_message.content = prompt_message_content[0].data
@@ -828,14 +828,14 @@ class LLMNode(BaseNode[LLMNodeData]):
         }
 
 
-def _combine_text_message_with_role(*, text: str, role: PromptMessageRole):
+def _combine_message_content_with_role(*, contents: Sequence[PromptMessageContent], role: PromptMessageRole):
     match role:
         case PromptMessageRole.USER:
-            return UserPromptMessage(content=[TextPromptMessageContent(data=text)])
+            return UserPromptMessage(content=contents)
         case PromptMessageRole.ASSISTANT:
-            return AssistantPromptMessage(content=[TextPromptMessageContent(data=text)])
+            return AssistantPromptMessage(content=contents)
         case PromptMessageRole.SYSTEM:
-            return SystemPromptMessage(content=[TextPromptMessageContent(data=text)])
+            return SystemPromptMessage(content=contents)
     raise NotImplementedError(f"Role {role} is not supported")
 
 
@@ -877,7 +877,9 @@ def _handle_list_messages(
                 jinjia2_variables=jinja2_variables,
                 variable_pool=variable_pool,
             )
-            prompt_message = _combine_text_message_with_role(text=result_text, role=message.role)
+            prompt_message = _combine_message_content_with_role(
+                contents=[TextPromptMessageContent(data=result_text)], role=message.role
+            )
             prompt_messages.append(prompt_message)
         else:
             # Get segment group from basic message
@@ -908,12 +910,14 @@ def _handle_list_messages(
             # Create message with text from all segments
             plain_text = segment_group.text
             if plain_text:
-                prompt_message = _combine_text_message_with_role(text=plain_text, role=message.role)
+                prompt_message = _combine_message_content_with_role(
+                    contents=[TextPromptMessageContent(data=plain_text)], role=message.role
+                )
                 prompt_messages.append(prompt_message)
 
             if file_contents:
                 # Create message with image contents
-                prompt_message = UserPromptMessage(content=file_contents)
+                prompt_message = _combine_message_content_with_role(contents=file_contents, role=message.role)
                 prompt_messages.append(prompt_message)
 
     return prompt_messages
@@ -1018,6 +1022,8 @@ def _handle_completion_template(
         else:
             template_text = template.text
         result_text = variable_pool.convert_template(template_text).text
-    prompt_message = _combine_text_message_with_role(text=result_text, role=PromptMessageRole.USER)
+    prompt_message = _combine_message_content_with_role(
+        contents=[TextPromptMessageContent(data=result_text)], role=PromptMessageRole.USER
+    )
     prompt_messages.append(prompt_message)
     return prompt_messages

api/libs/helper.py

@@ -31,12 +31,12 @@ class AppIconUrlField(fields.Raw):
         if obj is None:
             return None
 
-        from models.model import App, IconType
+        from models.model import App, IconType, Site
 
         if isinstance(obj, dict) and "app" in obj:
             obj = obj["app"]
 
-        if isinstance(obj, App) and obj.icon_type == IconType.IMAGE.value:
+        if isinstance(obj, App | Site) and obj.icon_type == IconType.IMAGE.value:
             return file_helpers.get_signed_file_url(obj.icon)
         return None
 

api/models/account.py

@@ -56,8 +56,8 @@ class Account(UserMixin, db.Model):
         self._current_tenant = tenant
 
     @property
-    def current_tenant_id(self):
-        return self._current_tenant.id
+    def current_tenant_id(self) -> str | None:
+        return self._current_tenant.id if self._current_tenant else None
 
     @current_tenant_id.setter
     def current_tenant_id(self, value: str):

api/models/workflow.py

@@ -1,7 +1,7 @@
 import json
 from collections.abc import Mapping, Sequence
 from datetime import UTC, datetime
-from enum import Enum
+from enum import Enum, StrEnum
 from typing import Any, Optional, Union
 
 import sqlalchemy as sa
@@ -314,7 +314,7 @@ class Workflow(db.Model):
         )
 
 
-class WorkflowRunStatus(Enum):
+class WorkflowRunStatus(StrEnum):
     """
     Workflow Run Status Enum
     """
@@ -393,13 +393,13 @@ class WorkflowRun(db.Model):
     version = db.Column(db.String(255), nullable=False)
     graph = db.Column(db.Text)
     inputs = db.Column(db.Text)
-    status = db.Column(db.String(255), nullable=False)
-    outputs: Mapped[str] = db.Column(db.Text)
+    status = db.Column(db.String(255), nullable=False)  # running, succeeded, failed, stopped
+    outputs: Mapped[str] = mapped_column(sa.Text, default="{}")
     error = db.Column(db.Text)
     elapsed_time = db.Column(db.Float, nullable=False, server_default=db.text("0"))
     total_tokens = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
     total_steps = db.Column(db.Integer, server_default=db.text("0"))
-    created_by_role = db.Column(db.String(255), nullable=False)
+    created_by_role = db.Column(db.String(255), nullable=False)  # account, end_user
     created_by = db.Column(StringUUID, nullable=False)
     created_at = db.Column(db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)"))
     finished_at = db.Column(db.DateTime)

api/services/conversation_service.py

@@ -1,3 +1,4 @@
+from collections.abc import Callable
 from datetime import UTC, datetime
 from typing import Optional, Union
 
@@ -74,14 +75,14 @@ class ConversationService:
         return InfiniteScrollPagination(data=conversations, limit=limit, has_more=has_more)
 
     @classmethod
-    def _get_sort_params(cls, sort_by: str) -> tuple[str, callable]:
+    def _get_sort_params(cls, sort_by: str):
         if sort_by.startswith("-"):
             return sort_by[1:], desc
         return sort_by, asc
 
     @classmethod
     def _build_filter_condition(
-        cls, sort_field: str, sort_direction: callable, reference_conversation: Conversation, is_next_page: bool = False
+        cls, sort_field: str, sort_direction: Callable, reference_conversation: Conversation, is_next_page: bool = False
     ):
         field_value = getattr(reference_conversation, sort_field)
         if (sort_direction == desc and not is_next_page) or (sort_direction == asc and is_next_page):
@@ -160,5 +161,5 @@ class ConversationService:
         conversation = cls.get_conversation(app_model, conversation_id, user)
 
         conversation.is_deleted = True
-        conversation.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
+        conversation.updated_at = datetime.now(UTC).replace(tzinfo=None)
         db.session.commit()

api/services/dataset_service.py

@@ -1390,7 +1390,7 @@ class SegmentService:
                 model=dataset.embedding_model,
             )
             # calc embedding use tokens
-            tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]
+            tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])
         lock_name = "add_segment_lock_document_id_{}".format(document.id)
         with redis_client.lock(lock_name, timeout=600):
             max_position = (
@@ -1467,11 +1467,9 @@ class SegmentService:
                 if dataset.indexing_technique == "high_quality" and embedding_model:
                     # calc embedding use tokens
                     if document.doc_form == "qa_model":
-                        tokens = embedding_model.get_text_embedding_num_tokens(
-                            texts=[content + segment_item["answer"]]
-                        )[0]
+                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content + segment_item["answer"]])
                     else:
-                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]
+                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])
                 segment_document = DocumentSegment(
                     tenant_id=current_user.current_tenant_id,
                     dataset_id=document.dataset_id,
@@ -1579,9 +1577,9 @@ class SegmentService:
 
                     # calc embedding use tokens
                     if document.doc_form == "qa_model":
-                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content + segment.answer])[0]
+                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content + segment.answer])
                     else:
-                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])[0]
+                        tokens = embedding_model.get_text_embedding_num_tokens(texts=[content])
                 segment.content = content
                 segment.index_node_hash = segment_hash
                 segment.word_count = len(content)

api/tasks/batch_create_segment_to_index_task.py

@@ -58,16 +58,12 @@ def batch_create_segment_to_index_task(
                 model=dataset.embedding_model,
             )
         word_count_change = 0
-        if embedding_model:
-            tokens_list = embedding_model.get_text_embedding_num_tokens(
-                texts=[segment["content"] for segment in content]
-            )
-        else:
-            tokens_list = [0] * len(content)
-        for segment, tokens in zip(content, tokens_list):
+        for segment in content:
             content = segment["content"]
             doc_id = str(uuid.uuid4())
             segment_hash = helper.generate_text_hash(content)
+            # calc embedding use tokens
+            tokens = embedding_model.get_text_embedding_num_tokens(texts=[content]) if embedding_model else 0
             max_position = (
                 db.session.query(func.max(DocumentSegment.position))
                 .filter(DocumentSegment.document_id == dataset_document.id)

docker-legacy/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3'
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.11.2
+    image: langgenius/dify-api:0.12.1
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
@@ -227,7 +227,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.11.2
+    image: langgenius/dify-api:0.12.1
     restart: always
     environment:
       CONSOLE_WEB_URL: ''
@@ -397,7 +397,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.11.2
+    image: langgenius/dify-web:0.12.1
     restart: always
     environment:
       # The base URL of console application api server, refers to the Console base URL of WEB service if console domain is

docker/docker-compose.yaml

@@ -291,7 +291,7 @@ x-shared-env: &shared-api-worker-env
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.11.2
+    image: langgenius/dify-api:0.12.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -311,7 +311,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.11.2
+    image: langgenius/dify-api:0.12.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -330,7 +330,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.11.2
+    image: langgenius/dify-web:0.12.1
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}

web/app/components/explore/app-list/index.tsx

@@ -14,7 +14,7 @@ import type { App } from '@/models/explore'
 import Category from '@/app/components/explore/category'
 import AppCard from '@/app/components/explore/app-card'
 import { fetchAppDetail, fetchAppList } from '@/service/explore'
-import { importApp } from '@/service/apps'
+import { importDSL } from '@/service/apps'
 import { useTabSearchParams } from '@/hooks/use-tab-searchparams'
 import CreateAppModal from '@/app/components/explore/create-app-modal'
 import AppTypeSelector from '@/app/components/app/type-selector'
@@ -24,6 +24,7 @@ import { NEED_REFRESH_APP_LIST_KEY } from '@/config'
 import { useAppContext } from '@/context/app-context'
 import { getRedirection } from '@/utils/app-redirection'
 import Input from '@/app/components/base/input'
+import { DSLImportMode } from '@/models/app'
 
 type AppsProps = {
   pageType?: PageType
@@ -127,8 +128,9 @@ const Apps = ({
       currApp?.app.id as string,
     )
     try {
-      const app = await importApp({
-        data: export_data,
+      const app = await importDSL({
+        mode: DSLImportMode.YAML_CONTENT,
+        yaml_content: export_data,
         name,
         icon_type,
         icon,
@@ -143,7 +145,7 @@ const Apps = ({
       if (onSuccess)
         onSuccess()
       localStorage.setItem(NEED_REFRESH_APP_LIST_KEY, '1')
-      getRedirection(isCurrentWorkspaceEditor, app, push)
+      getRedirection(isCurrentWorkspaceEditor, { id: app.app_id }, push)
     }
     catch (e) {
       Toast.notify({ type: 'error', message: t('app.newApp.appCreateFailed') })

web/package.json

@@ -1,6 +1,6 @@
 {
   "name": "dify-web",
-  "version": "0.11.2",
+  "version": "0.12.1",
   "private": true,
   "engines": {
     "node": ">=18.17.0"

web/themes/dark.css

@@ -85,6 +85,10 @@ html[data-theme="dark"] {
   --color-components-button-secondary-accent-border-hover: #FFFFFF1F;
   --color-components-button-secondary-accent-border-disabled: #FFFFFF0D;
 
+  --color-components-button-indigo-bg: #444CE7;
+  --color-components-button-indigo-bg-hover: #6172F3;
+  --color-components-button-indigo-bg-disabled: #FFFFFF08;
+
   --color-components-checkbox-icon: #FFFFFFF2;
   --color-components-checkbox-icon-disabled: #FFFFFF33;
   --color-components-checkbox-bg: #296DFF;
@@ -95,10 +99,11 @@ html[data-theme="dark"] {
   --color-components-checkbox-border-disabled: #FFFFFF03;
   --color-components-checkbox-bg-unchecked: #FFFFFF08;
   --color-components-checkbox-bg-unchecked-hover: #FFFFFF0D;
+  --color-components-checkbox-bg-disabled-checked: #155AEF33;
 
   --color-components-radio-border-checked: #296DFF;
   --color-components-radio-border-checked-hover: #5289FF;
-  --color-components-radio-border-checked-disabled: #FFFFFF14;
+  --color-components-radio-border-checked-disabled: #155AEF33;
   --color-components-radio-bg-disabled: #FFFFFF08;
   --color-components-radio-border: #FFFFFF66;
   --color-components-radio-border-hover: #FFFFFF99;
@@ -135,6 +140,9 @@ html[data-theme="dark"] {
   --color-components-panel-on-panel-item-bg: #27272B;
   --color-components-panel-on-panel-item-bg-hover: #3A3A40;
   --color-components-panel-on-panel-item-bg-alt: #3A3A40;
+  --color-components-panel-on-panel-item-bg-transparent: #2C2C30F2;
+  --color-components-panel-on-panel-item-bg-hover-transparent: #3A3A4000;
+  --color-components-panel-on-panel-item-bg-destructive-hover-transparent: #FFFBFA00;
 
   --color-components-panel-bg-transparent: #22222500;
 
@@ -208,10 +216,12 @@ html[data-theme="dark"] {
 
   --color-components-actionbar-bg: #222225;
   --color-components-actionbar-border: #C8CEDA14;
+  --color-components-actionbar-bg-accent: #27272B;
+  --color-components-actionbar-border-accent: #5289FF;
 
   --color-components-dropzone-bg-alt: #18181BCC;
   --color-components-dropzone-bg: #18181B66;
-  --color-components-dropzone-bg-accent: #155AEF24;
+  --color-components-dropzone-bg-accent: #155AEF33;
   --color-components-dropzone-border: #C8CEDA24;
   --color-components-dropzone-border-alt: #C8CEDA33;
   --color-components-dropzone-border-accent: #84ABFF;
@@ -228,6 +238,14 @@ html[data-theme="dark"] {
   --color-components-progress-gray-border: #98A2B2;
   --color-components-progress-gray-bg: #C8CEDA05;
 
+  --color-components-progress-warning-progress: #FDB022;
+  --color-components-progress-warning-border: #FDB022;
+  --color-components-progress-warning-bg: #F790090A;
+
+  --color-components-progress-error-progress: #F97066;
+  --color-components-progress-error-border: #F97066;
+  --color-components-progress-error-bg: #F044380A;
+
   --color-components-chat-input-audio-bg: #155AEF33;
   --color-components-chat-input-audio-wave-default: #C8CEDA24;
   --color-components-chat-input-bg-mask-1: #18181B0A;
@@ -236,13 +254,103 @@ html[data-theme="dark"] {
   --color-components-chat-input-audio-wave-active: #84ABFF;
   --color-components-chat-input-audio-bg-alt: #18181BE5;
 
-  --color-components-Avatar-shape-fill-stop-0: #FFFFFFF2;
-  --color-components-Avatar-shape-fill-stop-100: #FFFFFFCC;
+  --color-components-avatar-shape-fill-stop-0: #FFFFFFF2;
+  --color-components-avatar-shape-fill-stop-100: #FFFFFFCC;
 
-  --color-components-Avatar-bg-mask-stop-0: #FFFFFF33;
-  --color-components-Avatar-bg-mask-stop-100: #FFFFFF08;
+  --color-components-avatar-bg-mask-stop-0: #FFFFFF33;
+  --color-components-avatar-bg-mask-stop-100: #FFFFFF08;
 
-  --color-components-Avatar-default-avatar-bg: #222225;
+  --color-components-avatar-default-avatar-bg: #222225;
+  --color-components-avatar-mask-darkmode-dimmed: #0000001F;
+
+  --color-components-label-gray: #C8CEDA24;
+
+  --color-components-premium-badge-blue-bg-stop-0: #5289FF;
+  --color-components-premium-badge-blue-bg-stop-100: #296DFF;
+  --color-components-premium-badge-blue-stroke-stop-0: #FFFFFF33;
+  --color-components-premium-badge-blue-stroke-stop-100: #296DFF;
+  --color-components-premium-badge-blue-text-stop-0: #EFF4FF;
+  --color-components-premium-badge-blue-text-stop-100: #B2CAFF;
+  --color-components-premium-badge-blue-glow: #004AEB;
+  --color-components-premium-badge-blue-bg-stop-0-hover: #84ABFF;
+  --color-components-premium-badge-blue-bg-stop-100-hover: #004AEB;
+  --color-components-premium-badge-blue-glow-hover: #D1E0FF;
+  --color-components-premium-badge-blue-stroke-stop-0-hover: #FFFFFF80;
+  --color-components-premium-badge-blue-stroke-stop-100-hover: #296DFF;
+
+  --color-components-premium-badge-highlight-stop-0: #FFFFFF1F;
+  --color-components-premium-badge-highlight-stop-100: #FFFFFF33;
+  --color-components-premium-badge-indigo-bg-stop-0: #6172F3;
+  --color-components-premium-badge-indigo-bg-stop-100: #3538CD;
+  --color-components-premium-badge-indigo-stroke-stop-0: #FFFFFF33;
+  --color-components-premium-badge-indigo-stroke-stop-100: #444CE7;
+  --color-components-premium-badge-indigo-text-stop-0: #EEF4FF;
+  --color-components-premium-badge-indigo-text-stop-100: #C7D7FE;
+  --color-components-premium-badge-indigo-glow: #3538CD;
+  --color-components-premium-badge-indigo-glow-hover: #E0EAFF;
+  --color-components-premium-badge-indigo-bg-stop-0-hover: #A4BCFD;
+  --color-components-premium-badge-indigo-bg-stop-100-hover: #3538CD;
+  --color-components-premium-badge-indigo-stroke-stop-0-hover: #FFFFFF80;
+  --color-components-premium-badge-indigo-stroke-stop-100-hover: #444CE7;
+
+  --color-components-premium-badge-grey-bg-stop-0: #676F83;
+  --color-components-premium-badge-grey-bg-stop-100: #495464;
+  --color-components-premium-badge-grey-stroke-stop-0: #FFFFFF1F;
+  --color-components-premium-badge-grey-stroke-stop-100: #495464;
+  --color-components-premium-badge-grey-text-stop-0: #F9FAFB;
+  --color-components-premium-badge-grey-text-stop-100: #E9EBF0;
+  --color-components-premium-badge-grey-glow: #354052;
+  --color-components-premium-badge-grey-glow-hover: #F2F4F7;
+  --color-components-premium-badge-grey-bg-stop-0-hover: #98A2B2;
+  --color-components-premium-badge-grey-bg-stop-100-hover: #354052;
+  --color-components-premium-badge-grey-stroke-stop-0-hover: #FFFFFF80;
+  --color-components-premium-badge-grey-stroke-stop-100-hover: #676F83;
+
+  --color-components-premium-badge-orange-bg-stop-0: #FF692E;
+  --color-components-premium-badge-orange-bg-stop-100: #E04F16;
+  --color-components-premium-badge-orange-stroke-stop-0: #FFFFFF33;
+  --color-components-premium-badge-orange-stroke-stop-100: #FF4405;
+  --color-components-premium-badge-orange-text-stop-0: #FEF6EE;
+  --color-components-premium-badge-orange-text-stop-100: #F9DBAF;
+  --color-components-premium-badge-orange-glow: #B93815;
+  --color-components-premium-badge-orange-glow-hover: #FDEAD7;
+  --color-components-premium-badge-orange-bg-stop-0-hover: #FF692E;
+  --color-components-premium-badge-orange-bg-stop-100-hover: #B93815;
+  --color-components-premium-badge-orange-stroke-stop-0-hover: #FFFFFF80;
+  --color-components-premium-badge-orange-stroke-stop-100-hover: #FF4405;
+
+  --color-components-progress-bar-bg: #C8CEDA14;
+  --color-components-progress-bar-progress: #C8CEDA24;
+  --color-components-progress-bar-border: #FFFFFF08;
+  --color-components-progress-bar-progress-solid: #FFFFFFF2;
+  --color-components-progress-bar-progress-highlight: #C8CEDA33;
+
+  --color-components-icon-bg-red-solid: #D92D20;
+  --color-components-icon-bg-rose-solid: #E31B54;
+  --color-components-icon-bg-pink-solid: #DD2590;
+  --color-components-icon-bg-orange-dark-solid: #FF4405;
+  --color-components-icon-bg-yellow-solid: #EAAA08;
+  --color-components-icon-bg-green-solid: #4CA30D;
+  --color-components-icon-bg-teal-solid: #0E9384;
+  --color-components-icon-bg-blue-light-solid: #0BA5EC;
+  --color-components-icon-bg-blue-solid: #155AEF;
+  --color-components-icon-bg-indigo-solid: #444CE7;
+  --color-components-icon-bg-violet-solid: #7839EE;
+  --color-components-icon-bg-midnight-solid: #5D698D;
+  --color-components-icon-bg-rose-soft: #F63D6833;
+  --color-components-icon-bg-pink-soft: #EE46BC33;
+  --color-components-icon-bg-orange-dark-soft: #FF440533;
+  --color-components-icon-bg-yellow-soft: #EAAA0833;
+  --color-components-icon-bg-green-soft: #66C61C33;
+  --color-components-icon-bg-teal-soft: #15B79E33;
+  --color-components-icon-bg-blue-light-soft: #0BA5EC33;
+  --color-components-icon-bg-blue-soft: #155AEF33;
+  --color-components-icon-bg-indigo-soft: #6172F333;
+  --color-components-icon-bg-violet-soft: #875BF733;
+  --color-components-icon-bg-midnight-soft: #828DAD33;
+  --color-components-icon-bg-red-soft: #F0443833;
+  --color-components-icon-bg-orange-solid: #F79009;
+  --color-components-icon-bg-orange-soft: #F7900933;
 
   --color-text-primary: #FBFBFC;
   --color-text-secondary: #D9D9DE;
@@ -302,6 +410,7 @@ html[data-theme="dark"] {
   --color-background-overlay-alt: #18181B66;
   --color-background-surface-white: #FFFFFFE5;
   --color-background-overlay-destructive: #F044384D;
+  --color-background-overlay-backdrop: #18181BF2;
 
   --color-shadow-shadow-1: #0000000D;
   --color-shadow-shadow-3: #0000001A;
@@ -317,14 +426,20 @@ html[data-theme="dark"] {
   --color-workflow-block-border: #FFFFFF14;
   --color-workflow-block-parma-bg: #FFFFFF0D;
   --color-workflow-block-bg: #27272B;
+  --color-workflow-block-bg-transparent: #27272BF5;
   --color-workflow-block-border-highlight: #C8CEDA33;
 
   --color-workflow-canvas-workflow-dot-color: #8585AD26;
   --color-workflow-canvas-workflow-bg: #1D1D20;
 
-  --color-workflow-link-line-active: #296DFF;
+  --color-workflow-link-line-active: #5289FF;
   --color-workflow-link-line-normal: #676F83;
-  --color-workflow-link-line-handle: #296DFF;
+  --color-workflow-link-line-handle: #5289FF;
+  --color-workflow-link-line-normal-transparent: #676F8333;
+  --color-workflow-link-line-failure-active: #FDB022;
+  --color-workflow-link-line-failure-handle: #FDB022;
+  --color-workflow-link-line-failure-button-bg: #F79009;
+  --color-workflow-link-line-failure-button-hover: #DC6803;
 
   --color-workflow-link-line-success-active: #47CD89;
   --color-workflow-link-line-success-handle: #47CD89;
@@ -341,8 +456,8 @@ html[data-theme="dark"] {
   --color-workflow-display-success-vignette-color: #17B26A40;
   --color-workflow-display-success-bg-line-pattern: #18181BCC;
 
-  --color-workflow-display-glass-1: #FFFFFF03;
-  --color-workflow-display-glass-2: #FFFFFF08;
+  --color-workflow-display-glass-1: #FFFFFF08;
+  --color-workflow-display-glass-2: #FFFFFF0D;
   --color-workflow-display-vignette-dark: #00000066;
   --color-workflow-display-highlight: #FFFFFF1F;
   --color-workflow-display-outline: #18181BF2;
@@ -431,6 +546,7 @@ html[data-theme="dark"] {
   --color-util-colors-orange-orange-500: #EF6820;
   --color-util-colors-orange-orange-600: #F38744;
   --color-util-colors-orange-orange-700: #F7B27A;
+  --color-util-colors-orange-orange-100-transparent: #77291700;
 
   --color-util-colors-pink-pink-50: #4E0D30;
   --color-util-colors-pink-pink-100: #851651;
@@ -606,4 +722,16 @@ html[data-theme="dark"] {
   --color-third-party-LangChain: #FFFFFF;
   --color-third-party-Langfuse: #FFFFFF;
   --color-third-party-Github: #FFFFFF;
+  --color-third-party-Github-tertiary: #C8CEDA99;
+  --color-third-party-Github-secondary: #D9D9DE;
+  --color-third-party-model-bg-openai: #121212;
+  --color-third-party-model-bg-anthropic: #1D1917;
+  --color-third-party-model-bg-default: #0B0B0E;
+
+  --color-third-party-aws: #141F2E;
+  --color-third-party-aws-alt: #192639;
+
+  --color-saas-background: #0B0B0E;
+  --color-saas-pricing-grid-bg: #C8CEDA33;
+
 }

web/themes/light.css

@@ -85,8 +85,12 @@ html[data-theme="light"] {
   --color-components-button-secondary-accent-border-hover: #10182824;
   --color-components-button-secondary-accent-border-disabled: #1018280A;
 
+  --color-components-button-indigo-bg: #444CE7;
+  --color-components-button-indigo-bg-hover: #3538CD;
+  --color-components-button-indigo-bg-disabled: #6172F324;
+
   --color-components-checkbox-icon: #FFFFFF;
-  --color-components-checkbox-icon-disabled: #D0D5DC;
+  --color-components-checkbox-icon-disabled: #FFFFFF80;
   --color-components-checkbox-bg: #155AEF;
   --color-components-checkbox-bg-hover: #004AEB;
   --color-components-checkbox-bg-disabled: #F2F4F7;
@@ -95,10 +99,11 @@ html[data-theme="light"] {
   --color-components-checkbox-border-disabled: #18181B0A;
   --color-components-checkbox-bg-unchecked: #FFFFFF;
   --color-components-checkbox-bg-unchecked-hover: #FFFFFF;
+  --color-components-checkbox-bg-disabled-checked: #B2CAFF;
 
   --color-components-radio-border-checked: #155AEF;
   --color-components-radio-border-checked-hover: #004AEB;
-  --color-components-radio-border-checked-disabled: #F2F4F7;
+  --color-components-radio-border-checked-disabled: #B2CAFF;
   --color-components-radio-bg-disabled: #FFFFFF00;
   --color-components-radio-border: #D0D5DC;
   --color-components-radio-border-hover: #98A2B2;
@@ -135,6 +140,9 @@ html[data-theme="light"] {
   --color-components-panel-on-panel-item-bg: #FFFFFF;
   --color-components-panel-on-panel-item-bg-hover: #F9FAFB;
   --color-components-panel-on-panel-item-bg-alt: #F9FAFB;
+  --color-components-panel-on-panel-item-bg-transparent: #FFFFFFF2;
+  --color-components-panel-on-panel-item-bg-hover-transparent: #F9FAFB00;
+  --color-components-panel-on-panel-item-bg-destructive-hover-transparent: #FEF3F200;
 
   --color-components-panel-bg-transparent: #FFFFFF00;
 
@@ -161,10 +169,10 @@ html[data-theme="light"] {
   --color-components-segmented-control-item-active-accent-bg: #FFFFFF;
   --color-components-segmented-control-item-active-accent-border: #FFFFFF;
 
-  --color-components-option-card-option-bg: #F9FAFB;
+  --color-components-option-card-option-bg: #FCFCFD;
   --color-components-option-card-option-selected-bg: #FFFFFF;
   --color-components-option-card-option-selected-border: #296DFF;
-  --color-components-option-card-option-border: #F2F4F7;
+  --color-components-option-card-option-border: #E9EBF0;
   --color-components-option-card-option-bg-hover: #FFFFFF;
   --color-components-option-card-option-border-hover: #D0D5DC;
 
@@ -208,10 +216,12 @@ html[data-theme="light"] {
 
   --color-components-actionbar-bg: #FFFFFFF2;
   --color-components-actionbar-border: #1018280A;
+  --color-components-actionbar-bg-accent: #F5F7FF;
+  --color-components-actionbar-border-accent: #B2CAFF;
 
   --color-components-dropzone-bg-alt: #F2F4F7;
   --color-components-dropzone-bg: #F9FAFB;
-  --color-components-dropzone-bg-accent: #EFF4FF;
+  --color-components-dropzone-bg-accent: #155AEF24;
   --color-components-dropzone-border: #10182814;
   --color-components-dropzone-border-alt: #10182833;
   --color-components-dropzone-border-accent: #84ABFF;
@@ -228,6 +238,14 @@ html[data-theme="light"] {
   --color-components-progress-gray-border: #98A2B2;
   --color-components-progress-gray-bg: #C8CEDA05;
 
+  --color-components-progress-warning-progress: #F79009;
+  --color-components-progress-warning-border: #F79009;
+  --color-components-progress-warning-bg: #F790090A;
+
+  --color-components-progress-error-progress: #F04438;
+  --color-components-progress-error-border: #F04438;
+  --color-components-progress-error-bg: #F044380A;
+
   --color-components-chat-input-audio-bg: #EFF4FF;
   --color-components-chat-input-audio-wave-default: #155AEF33;
   --color-components-chat-input-bg-mask-1: #FFFFFF03;
@@ -236,13 +254,103 @@ html[data-theme="light"] {
   --color-components-chat-input-audio-wave-active: #296DFF;
   --color-components-chat-input-audio-bg-alt: #FCFCFD;
 
-  --color-components-Avatar-shape-fill-stop-0: #FFFFFF;
-  --color-components-Avatar-shape-fill-stop-100: #FFFFFFE5;
+  --color-components-avatar-shape-fill-stop-0: #FFFFFF;
+  --color-components-avatar-shape-fill-stop-100: #FFFFFFE5;
 
-  --color-components-Avatar-bg-mask-stop-0: #FFFFFF1F;
-  --color-components-Avatar-bg-mask-stop-100: #FFFFFF14;
+  --color-components-avatar-bg-mask-stop-0: #FFFFFF1F;
+  --color-components-avatar-bg-mask-stop-100: #FFFFFF14;
 
-  --color-components-Avatar-default-avatar-bg: #D0D5DC;
+  --color-components-avatar-default-avatar-bg: #D0D5DC;
+  --color-components-avatar-mask-darkmode-dimmed: #FFFFFF00;
+
+  --color-components-label-gray: #F2F4F7;
+
+  --color-components-premium-badge-blue-bg-stop-0: #5289FF;
+  --color-components-premium-badge-blue-bg-stop-100: #155AEF;
+  --color-components-premium-badge-blue-stroke-stop-0: #FFFFFFF2;
+  --color-components-premium-badge-blue-stroke-stop-100: #155AEF;
+  --color-components-premium-badge-blue-text-stop-0: #F5F7FF;
+  --color-components-premium-badge-blue-text-stop-100: #D1E0FF;
+  --color-components-premium-badge-blue-glow: #00329E;
+  --color-components-premium-badge-blue-bg-stop-0-hover: #296DFF;
+  --color-components-premium-badge-blue-bg-stop-100-hover: #004AEB;
+  --color-components-premium-badge-blue-glow-hover: #84ABFF;
+  --color-components-premium-badge-blue-stroke-stop-0-hover: #FFFFFFF2;
+  --color-components-premium-badge-blue-stroke-stop-100-hover: #00329E;
+
+  --color-components-premium-badge-highlight-stop-0: #FFFFFF1F;
+  --color-components-premium-badge-highlight-stop-100: #FFFFFF4D;
+  --color-components-premium-badge-indigo-bg-stop-0: #8098F9;
+  --color-components-premium-badge-indigo-bg-stop-100: #444CE7;
+  --color-components-premium-badge-indigo-stroke-stop-0: #FFFFFFF2;
+  --color-components-premium-badge-indigo-stroke-stop-100: #6172F3;
+  --color-components-premium-badge-indigo-text-stop-0: #F5F8FF;
+  --color-components-premium-badge-indigo-text-stop-100: #E0EAFF;
+  --color-components-premium-badge-indigo-glow: #2D3282;
+  --color-components-premium-badge-indigo-glow-hover: #A4BCFD;
+  --color-components-premium-badge-indigo-bg-stop-0-hover: #6172F3;
+  --color-components-premium-badge-indigo-bg-stop-100-hover: #2D31A6;
+  --color-components-premium-badge-indigo-stroke-stop-0-hover: #FFFFFFF2;
+  --color-components-premium-badge-indigo-stroke-stop-100-hover: #2D31A6;
+
+  --color-components-premium-badge-grey-bg-stop-0: #98A2B2;
+  --color-components-premium-badge-grey-bg-stop-100: #676F83;
+  --color-components-premium-badge-grey-stroke-stop-0: #FFFFFFF2;
+  --color-components-premium-badge-grey-stroke-stop-100: #676F83;
+  --color-components-premium-badge-grey-text-stop-0: #FCFCFD;
+  --color-components-premium-badge-grey-text-stop-100: #F2F4F7;
+  --color-components-premium-badge-grey-glow: #101828;
+  --color-components-premium-badge-grey-glow-hover: #D0D5DC;
+  --color-components-premium-badge-grey-bg-stop-0-hover: #676F83;
+  --color-components-premium-badge-grey-bg-stop-100-hover: #354052;
+  --color-components-premium-badge-grey-stroke-stop-0-hover: #FFFFFFF2;
+  --color-components-premium-badge-grey-stroke-stop-100-hover: #354052;
+
+  --color-components-premium-badge-orange-bg-stop-0: #FF692E;
+  --color-components-premium-badge-orange-bg-stop-100: #E04F16;
+  --color-components-premium-badge-orange-stroke-stop-0: #FFFFFFF2;
+  --color-components-premium-badge-orange-stroke-stop-100: #E62E05;
+  --color-components-premium-badge-orange-text-stop-0: #FEFAF5;
+  --color-components-premium-badge-orange-text-stop-100: #FDEAD7;
+  --color-components-premium-badge-orange-glow: #772917;
+  --color-components-premium-badge-orange-glow-hover: #F7B27A;
+  --color-components-premium-badge-orange-bg-stop-0-hover: #FF4405;
+  --color-components-premium-badge-orange-bg-stop-100-hover: #B93815;
+  --color-components-premium-badge-orange-stroke-stop-0-hover: #FFFFFFF2;
+  --color-components-premium-badge-orange-stroke-stop-100-hover: #BC1B06;
+
+  --color-components-progress-bar-bg: #155AEF0A;
+  --color-components-progress-bar-progress: #155AEF24;
+  --color-components-progress-bar-border: #1018280A;
+  --color-components-progress-bar-progress-solid: #296DFF;
+  --color-components-progress-bar-progress-highlight: #155AEF33;
+
+  --color-components-icon-bg-red-solid: #D92D20;
+  --color-components-icon-bg-rose-solid: #E31B54;
+  --color-components-icon-bg-pink-solid: #DD2590;
+  --color-components-icon-bg-orange-dark-solid: #FF4405;
+  --color-components-icon-bg-yellow-solid: #EAAA08;
+  --color-components-icon-bg-green-solid: #4CA30D;
+  --color-components-icon-bg-teal-solid: #0E9384;
+  --color-components-icon-bg-blue-light-solid: #0BA5EC;
+  --color-components-icon-bg-blue-solid: #155AEF;
+  --color-components-icon-bg-indigo-solid: #444CE7;
+  --color-components-icon-bg-violet-solid: #7839EE;
+  --color-components-icon-bg-midnight-solid: #828DAD;
+  --color-components-icon-bg-rose-soft: #FFF1F3;
+  --color-components-icon-bg-pink-soft: #FDF2FA;
+  --color-components-icon-bg-orange-dark-soft: #FFF4ED;
+  --color-components-icon-bg-yellow-soft: #FEFBE8;
+  --color-components-icon-bg-green-soft: #F3FEE7;
+  --color-components-icon-bg-teal-soft: #F0FDF9;
+  --color-components-icon-bg-blue-light-soft: #F0F9FF;
+  --color-components-icon-bg-blue-soft: #EFF4FF;
+  --color-components-icon-bg-indigo-soft: #EEF4FF;
+  --color-components-icon-bg-violet-soft: #F5F3FF;
+  --color-components-icon-bg-midnight-soft: #F0F2F5;
+  --color-components-icon-bg-red-soft: #FEF3F2;
+  --color-components-icon-bg-orange-solid: #F79009;
+  --color-components-icon-bg-orange-soft: #FFFAEB;
 
   --color-text-primary: #101828;
   --color-text-secondary: #354052;
@@ -302,6 +410,7 @@ html[data-theme="light"] {
   --color-background-overlay-alt: #10182866;
   --color-background-surface-white: #FFFFFFF2;
   --color-background-overlay-destructive: #F044384D;
+  --color-background-overlay-backdrop: #F2F4F7F2;
 
   --color-shadow-shadow-1: #09090B08;
   --color-shadow-shadow-3: #09090B0D;
@@ -317,6 +426,7 @@ html[data-theme="light"] {
   --color-workflow-block-border: #FFFFFF;
   --color-workflow-block-parma-bg: #F2F4F7;
   --color-workflow-block-bg: #FCFCFD;
+  --color-workflow-block-bg-transparent: #FCFCFDE5;
   --color-workflow-block-border-highlight: #155AEF24;
 
   --color-workflow-canvas-workflow-dot-color: #8585AD26;
@@ -436,6 +546,7 @@ html[data-theme="light"] {
   --color-util-colors-orange-orange-500: #EF6820;
   --color-util-colors-orange-orange-600: #E04F16;
   --color-util-colors-orange-orange-700: #B93815;
+  --color-util-colors-orange-orange-100-transparent: #FDEAD700;
 
   --color-util-colors-pink-pink-50: #FDF2FA;
   --color-util-colors-pink-pink-100: #FCE7F6;
@@ -610,6 +721,17 @@ html[data-theme="light"] {
 
   --color-third-party-LangChain: #1C3C3C;
   --color-third-party-Langfuse: #000000;
-
   --color-third-party-Github: #1B1F24;
+  --color-third-party-Github-tertiary: #1B1F24;
+  --color-third-party-Github-secondary: #1B1F24;
+  --color-third-party-model-bg-openai: #E3E5E8;
+  --color-third-party-model-bg-anthropic: #EEEDE7;
+  --color-third-party-model-bg-default: #F9FAFB;
+
+  --color-third-party-aws: #141F2E;
+  --color-third-party-aws-alt: #0F1824;
+
+  --color-saas-background: #FCFCFD;
+  --color-saas-pricing-grid-bg: #C8CEDA80;
+
 }

web/themes/tailwind-theme-var-define.ts

@@ -85,6 +85,10 @@ const vars = {
   'components-button-secondary-accent-border-hover': 'var(--color-components-button-secondary-accent-border-hover)',
   'components-button-secondary-accent-border-disabled': 'var(--color-components-button-secondary-accent-border-disabled)',
 
+  'components-button-indigo-bg': 'var(--color-components-button-indigo-bg)',
+  'components-button-indigo-bg-hover': 'var(--color-components-button-indigo-bg-hover)',
+  'components-button-indigo-bg-disabled': 'var(--color-components-button-indigo-bg-disabled)',
+
   'components-checkbox-icon': 'var(--color-components-checkbox-icon)',
   'components-checkbox-icon-disabled': 'var(--color-components-checkbox-icon-disabled)',
   'components-checkbox-bg': 'var(--color-components-checkbox-bg)',
@@ -95,6 +99,7 @@ const vars = {
   'components-checkbox-border-disabled': 'var(--color-components-checkbox-border-disabled)',
   'components-checkbox-bg-unchecked': 'var(--color-components-checkbox-bg-unchecked)',
   'components-checkbox-bg-unchecked-hover': 'var(--color-components-checkbox-bg-unchecked-hover)',
+  'components-checkbox-bg-disabled-checked': 'var(--color-components-checkbox-bg-disabled-checked)',
 
   'components-radio-border-checked': 'var(--color-components-radio-border-checked)',
   'components-radio-border-checked-hover': 'var(--color-components-radio-border-checked-hover)',
@@ -135,6 +140,9 @@ const vars = {
   'components-panel-on-panel-item-bg': 'var(--color-components-panel-on-panel-item-bg)',
   'components-panel-on-panel-item-bg-hover': 'var(--color-components-panel-on-panel-item-bg-hover)',
   'components-panel-on-panel-item-bg-alt': 'var(--color-components-panel-on-panel-item-bg-alt)',
+  'components-panel-on-panel-item-bg-transparent': 'var(--color-components-panel-on-panel-item-bg-transparent)',
+  'components-panel-on-panel-item-bg-hover-transparent': 'var(--color-components-panel-on-panel-item-bg-hover-transparent)',
+  'components-panel-on-panel-item-bg-destructive-hover-transparent': 'var(--color-components-panel-on-panel-item-bg-destructive-hover-transparent)',
 
   'components-panel-bg-transparent': 'var(--color-components-panel-bg-transparent)',
 
@@ -208,6 +216,8 @@ const vars = {
 
   'components-actionbar-bg': 'var(--color-components-actionbar-bg)',
   'components-actionbar-border': 'var(--color-components-actionbar-border)',
+  'components-actionbar-bg-accent': 'var(--color-components-actionbar-bg-accent)',
+  'components-actionbar-border-accent': 'var(--color-components-actionbar-border-accent)',
 
   'components-dropzone-bg-alt': 'var(--color-components-dropzone-bg-alt)',
   'components-dropzone-bg': 'var(--color-components-dropzone-bg)',
@@ -228,6 +238,14 @@ const vars = {
   'components-progress-gray-border': 'var(--color-components-progress-gray-border)',
   'components-progress-gray-bg': 'var(--color-components-progress-gray-bg)',
 
+  'components-progress-warning-progress': 'var(--color-components-progress-warning-progress)',
+  'components-progress-warning-border': 'var(--color-components-progress-warning-border)',
+  'components-progress-warning-bg': 'var(--color-components-progress-warning-bg)',
+
+  'components-progress-error-progress': 'var(--color-components-progress-error-progress)',
+  'components-progress-error-border': 'var(--color-components-progress-error-border)',
+  'components-progress-error-bg': 'var(--color-components-progress-error-bg)',
+
   'components-chat-input-audio-bg': 'var(--color-components-chat-input-audio-bg)',
   'components-chat-input-audio-wave-default': 'var(--color-components-chat-input-audio-wave-default)',
   'components-chat-input-bg-mask-1': 'var(--color-components-chat-input-bg-mask-1)',
@@ -236,13 +254,103 @@ const vars = {
   'components-chat-input-audio-wave-active': 'var(--color-components-chat-input-audio-wave-active)',
   'components-chat-input-audio-bg-alt': 'var(--color-components-chat-input-audio-bg-alt)',
 
-  'components-Avatar-shape-fill-stop-0': 'var(--color-components-Avatar-shape-fill-stop-0)',
-  'components-Avatar-shape-fill-stop-100': 'var(--color-components-Avatar-shape-fill-stop-100)',
+  'components-avatar-shape-fill-stop-0': 'var(--color-components-avatar-shape-fill-stop-0)',
+  'components-avatar-shape-fill-stop-100': 'var(--color-components-avatar-shape-fill-stop-100)',
 
-  'components-Avatar-bg-mask-stop-0': 'var(--color-components-Avatar-bg-mask-stop-0)',
-  'components-Avatar-bg-mask-stop-100': 'var(--color-components-Avatar-bg-mask-stop-100)',
+  'components-avatar-bg-mask-stop-0': 'var(--color-components-avatar-bg-mask-stop-0)',
+  'components-avatar-bg-mask-stop-100': 'var(--color-components-avatar-bg-mask-stop-100)',
 
-  'components-Avatar-default-avatar-bg': 'var(--color-components-Avatar-default-avatar-bg)',
+  'components-avatar-default-avatar-bg': 'var(--color-components-avatar-default-avatar-bg)',
+  'components-avatar-mask-darkmode-dimmed': 'var(--color-components-avatar-mask-darkmode-dimmed)',
+
+  'components-label-gray': 'var(--color-components-label-gray)',
+
+  'components-premium-badge-blue-bg-stop-0': 'var(--color-components-premium-badge-blue-bg-stop-0)',
+  'components-premium-badge-blue-bg-stop-100': 'var(--color-components-premium-badge-blue-bg-stop-100)',
+  'components-premium-badge-blue-stroke-stop-0': 'var(--color-components-premium-badge-blue-stroke-stop-0)',
+  'components-premium-badge-blue-stroke-stop-100': 'var(--color-components-premium-badge-blue-stroke-stop-100)',
+  'components-premium-badge-blue-text-stop-0': 'var(--color-components-premium-badge-blue-text-stop-0)',
+  'components-premium-badge-blue-text-stop-100': 'var(--color-components-premium-badge-blue-text-stop-100)',
+  'components-premium-badge-blue-glow': 'var(--color-components-premium-badge-blue-glow)',
+  'components-premium-badge-blue-bg-stop-0-hover': 'var(--color-components-premium-badge-blue-bg-stop-0-hover)',
+  'components-premium-badge-blue-bg-stop-100-hover': 'var(--color-components-premium-badge-blue-bg-stop-100-hover)',
+  'components-premium-badge-blue-glow-hover': 'var(--color-components-premium-badge-blue-glow-hover)',
+  'components-premium-badge-blue-stroke-stop-0-hover': 'var(--color-components-premium-badge-blue-stroke-stop-0-hover)',
+  'components-premium-badge-blue-stroke-stop-100-hover': 'var(--color-components-premium-badge-blue-stroke-stop-100-hover)',
+
+  'components-premium-badge-highlight-stop-0': 'var(--color-components-premium-badge-highlight-stop-0)',
+  'components-premium-badge-highlight-stop-100': 'var(--color-components-premium-badge-highlight-stop-100)',
+  'components-premium-badge-indigo-bg-stop-0': 'var(--color-components-premium-badge-indigo-bg-stop-0)',
+  'components-premium-badge-indigo-bg-stop-100': 'var(--color-components-premium-badge-indigo-bg-stop-100)',
+  'components-premium-badge-indigo-stroke-stop-0': 'var(--color-components-premium-badge-indigo-stroke-stop-0)',
+  'components-premium-badge-indigo-stroke-stop-100': 'var(--color-components-premium-badge-indigo-stroke-stop-100)',
+  'components-premium-badge-indigo-text-stop-0': 'var(--color-components-premium-badge-indigo-text-stop-0)',
+  'components-premium-badge-indigo-text-stop-100': 'var(--color-components-premium-badge-indigo-text-stop-100)',
+  'components-premium-badge-indigo-glow': 'var(--color-components-premium-badge-indigo-glow)',
+  'components-premium-badge-indigo-glow-hover': 'var(--color-components-premium-badge-indigo-glow-hover)',
+  'components-premium-badge-indigo-bg-stop-0-hover': 'var(--color-components-premium-badge-indigo-bg-stop-0-hover)',
+  'components-premium-badge-indigo-bg-stop-100-hover': 'var(--color-components-premium-badge-indigo-bg-stop-100-hover)',
+  'components-premium-badge-indigo-stroke-stop-0-hover': 'var(--color-components-premium-badge-indigo-stroke-stop-0-hover)',
+  'components-premium-badge-indigo-stroke-stop-100-hover': 'var(--color-components-premium-badge-indigo-stroke-stop-100-hover)',
+
+  'components-premium-badge-grey-bg-stop-0': 'var(--color-components-premium-badge-grey-bg-stop-0)',
+  'components-premium-badge-grey-bg-stop-100': 'var(--color-components-premium-badge-grey-bg-stop-100)',
+  'components-premium-badge-grey-stroke-stop-0': 'var(--color-components-premium-badge-grey-stroke-stop-0)',
+  'components-premium-badge-grey-stroke-stop-100': 'var(--color-components-premium-badge-grey-stroke-stop-100)',
+  'components-premium-badge-grey-text-stop-0': 'var(--color-components-premium-badge-grey-text-stop-0)',
+  'components-premium-badge-grey-text-stop-100': 'var(--color-components-premium-badge-grey-text-stop-100)',
+  'components-premium-badge-grey-glow': 'var(--color-components-premium-badge-grey-glow)',
+  'components-premium-badge-grey-glow-hover': 'var(--color-components-premium-badge-grey-glow-hover)',
+  'components-premium-badge-grey-bg-stop-0-hover': 'var(--color-components-premium-badge-grey-bg-stop-0-hover)',
+  'components-premium-badge-grey-bg-stop-100-hover': 'var(--color-components-premium-badge-grey-bg-stop-100-hover)',
+  'components-premium-badge-grey-stroke-stop-0-hover': 'var(--color-components-premium-badge-grey-stroke-stop-0-hover)',
+  'components-premium-badge-grey-stroke-stop-100-hover': 'var(--color-components-premium-badge-grey-stroke-stop-100-hover)',
+
+  'components-premium-badge-orange-bg-stop-0': 'var(--color-components-premium-badge-orange-bg-stop-0)',
+  'components-premium-badge-orange-bg-stop-100': 'var(--color-components-premium-badge-orange-bg-stop-100)',
+  'components-premium-badge-orange-stroke-stop-0': 'var(--color-components-premium-badge-orange-stroke-stop-0)',
+  'components-premium-badge-orange-stroke-stop-100': 'var(--color-components-premium-badge-orange-stroke-stop-100)',
+  'components-premium-badge-orange-text-stop-0': 'var(--color-components-premium-badge-orange-text-stop-0)',
+  'components-premium-badge-orange-text-stop-100': 'var(--color-components-premium-badge-orange-text-stop-100)',
+  'components-premium-badge-orange-glow': 'var(--color-components-premium-badge-orange-glow)',
+  'components-premium-badge-orange-glow-hover': 'var(--color-components-premium-badge-orange-glow-hover)',
+  'components-premium-badge-orange-bg-stop-0-hover': 'var(--color-components-premium-badge-orange-bg-stop-0-hover)',
+  'components-premium-badge-orange-bg-stop-100-hover': 'var(--color-components-premium-badge-orange-bg-stop-100-hover)',
+  'components-premium-badge-orange-stroke-stop-0-hover': 'var(--color-components-premium-badge-orange-stroke-stop-0-hover)',
+  'components-premium-badge-orange-stroke-stop-100-hover': 'var(--color-components-premium-badge-orange-stroke-stop-100-hover)',
+
+  'components-progress-bar-bg': 'var(--color-components-progress-bar-bg)',
+  'components-progress-bar-progress': 'var(--color-components-progress-bar-progress)',
+  'components-progress-bar-border': 'var(--color-components-progress-bar-border)',
+  'components-progress-bar-progress-solid': 'var(--color-components-progress-bar-progress-solid)',
+  'components-progress-bar-progress-highlight': 'var(--color-components-progress-bar-progress-highlight)',
+
+  'components-icon-bg-red-solid': 'var(--color-components-icon-bg-red-solid)',
+  'components-icon-bg-rose-solid': 'var(--color-components-icon-bg-rose-solid)',
+  'components-icon-bg-pink-solid': 'var(--color-components-icon-bg-pink-solid)',
+  'components-icon-bg-orange-dark-solid': 'var(--color-components-icon-bg-orange-dark-solid)',
+  'components-icon-bg-yellow-solid': 'var(--color-components-icon-bg-yellow-solid)',
+  'components-icon-bg-green-solid': 'var(--color-components-icon-bg-green-solid)',
+  'components-icon-bg-teal-solid': 'var(--color-components-icon-bg-teal-solid)',
+  'components-icon-bg-blue-light-solid': 'var(--color-components-icon-bg-blue-light-solid)',
+  'components-icon-bg-blue-solid': 'var(--color-components-icon-bg-blue-solid)',
+  'components-icon-bg-indigo-solid': 'var(--color-components-icon-bg-indigo-solid)',
+  'components-icon-bg-violet-solid': 'var(--color-components-icon-bg-violet-solid)',
+  'components-icon-bg-midnight-solid': 'var(--color-components-icon-bg-midnight-solid)',
+  'components-icon-bg-rose-soft': 'var(--color-components-icon-bg-rose-soft)',
+  'components-icon-bg-pink-soft': 'var(--color-components-icon-bg-pink-soft)',
+  'components-icon-bg-orange-dark-soft': 'var(--color-components-icon-bg-orange-dark-soft)',
+  'components-icon-bg-yellow-soft': 'var(--color-components-icon-bg-yellow-soft)',
+  'components-icon-bg-green-soft': 'var(--color-components-icon-bg-green-soft)',
+  'components-icon-bg-teal-soft': 'var(--color-components-icon-bg-teal-soft)',
+  'components-icon-bg-blue-light-soft': 'var(--color-components-icon-bg-blue-light-soft)',
+  'components-icon-bg-blue-soft': 'var(--color-components-icon-bg-blue-soft)',
+  'components-icon-bg-indigo-soft': 'var(--color-components-icon-bg-indigo-soft)',
+  'components-icon-bg-violet-soft': 'var(--color-components-icon-bg-violet-soft)',
+  'components-icon-bg-midnight-soft': 'var(--color-components-icon-bg-midnight-soft)',
+  'components-icon-bg-red-soft': 'var(--color-components-icon-bg-red-soft)',
+  'components-icon-bg-orange-solid': 'var(--color-components-icon-bg-orange-solid)',
+  'components-icon-bg-orange-soft': 'var(--color-components-icon-bg-orange-soft)',
 
   'text-primary': 'var(--color-text-primary)',
   'text-secondary': 'var(--color-text-secondary)',
@@ -302,6 +410,7 @@ const vars = {
   'background-overlay-alt': 'var(--color-background-overlay-alt)',
   'background-surface-white': 'var(--color-background-surface-white)',
   'background-overlay-destructive': 'var(--color-background-overlay-destructive)',
+  'background-overlay-backdrop': 'var(--color-background-overlay-backdrop)',
 
   'shadow-shadow-1': 'var(--color-shadow-shadow-1)',
   'shadow-shadow-3': 'var(--color-shadow-shadow-3)',
@@ -317,6 +426,7 @@ const vars = {
   'workflow-block-border': 'var(--color-workflow-block-border)',
   'workflow-block-parma-bg': 'var(--color-workflow-block-parma-bg)',
   'workflow-block-bg': 'var(--color-workflow-block-bg)',
+  'workflow-block-bg-transparent': 'var(--color-workflow-block-bg-transparent)',
   'workflow-block-border-highlight': 'var(--color-workflow-block-border-highlight)',
 
   'workflow-canvas-workflow-dot-color': 'var(--color-workflow-canvas-workflow-dot-color)',
@@ -436,6 +546,7 @@ const vars = {
   'util-colors-orange-orange-500': 'var(--color-util-colors-orange-orange-500)',
   'util-colors-orange-orange-600': 'var(--color-util-colors-orange-orange-600)',
   'util-colors-orange-orange-700': 'var(--color-util-colors-orange-orange-700)',
+  'util-colors-orange-orange-100-transparent': 'var(--color-util-colors-orange-orange-100-transparent)',
 
   'util-colors-pink-pink-50': 'var(--color-util-colors-pink-pink-50)',
   'util-colors-pink-pink-100': 'var(--color-util-colors-pink-pink-100)',
@@ -611,6 +722,17 @@ const vars = {
   'third-party-LangChain': 'var(--color-third-party-LangChain)',
   'third-party-Langfuse': 'var(--color-third-party-Langfuse)',
   'third-party-Github': 'var(--color-third-party-Github)',
-}
+  'third-party-Github-tertiary': 'var(--color-third-party-Github-tertiary)',
+  'third-party-Github-secondary': 'var(--color-third-party-Github-secondary)',
+  'third-party-model-bg-openai': 'var(--color-third-party-model-bg-openai)',
+  'third-party-model-bg-anthropic': 'var(--color-third-party-model-bg-anthropic)',
+  'third-party-model-bg-default': 'var(--color-third-party-model-bg-default)',
 
+  'third-party-aws': 'var(--color-third-party-aws)',
+  'third-party-aws-alt': 'var(--color-third-party-aws-alt)',
+
+  'saas-background': 'var(--color-saas-background)',
+  'saas-pricing-grid-bg': 'var(--color-saas-pricing-grid-bg)',
+
+}
 export default vars